将一个数据集按照一定的比例，划分为训练集，验证集和测试集，请使用python写出对应的代码

当你运行代码时，你需要将数据集的路径和划分比例修改为你自己的数据集和比例。

import os
import random
import shutil


def split_dataset(dataset_path, train_ratio=0.8, val_ratio=0.1, test_ratio=0.1):
    assert train_ratio + val_ratio + test_ratio == 1.0, 'train_ratio, val_ratio and test_ratio must sum up to 1.0'

    # 创建目标文件夹
    train_path = os.path.join(dataset_path, 'train')
    val_path = os.path.join(dataset_path, 'val')
    test_path = os.path.join(dataset_path, 'test')
    for path in [train_path, val_path, test_path]:
        if not os.path.exists(path):
            os.makedirs(path)

    # 获取所有文件路径
    file_paths = []
    for root, _, files in os.walk(dataset_path):
        for file in files:
            file_paths.append(os.path.join(root, file))

    # 打乱文件路径顺序
    random.shuffle(file_paths)

    # 根据比例划分数据集
    train_ratio_start = 0
    val_ratio_start = int(len(file_paths) * train_ratio)
    test_ratio_start = int(len(file_paths) * (train_ratio + val_ratio))
    for i, path in enumerate(file_paths):
        if i < val_ratio_start:
            shutil.copy(path, os.path.join(train_path, os.path.basename(path)))
        elif i < test_ratio_start:
            shutil.copy(path, os.path.join(val_path, os.path.basename(path)))
        else:
            shutil.copy(path, os.path.join(test_path, os.path.basename(path)))


if __name__ == '__main__':
    dataset_path = '/path/to/dataset'
    train_ratio = 0.8
    val_ratio = 0.1
    test_ratio = 0.1
    split_dataset(dataset_path, train_ratio, val_ratio, test_ratio)